A Fault Diagnostic Approach for Underwater Thrusters Based on Generative Adversarial Network-Reference-Cited by-同舟云学术

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A Fault Diagnostic Approach for Underwater Thrusters Based on Generative Adversarial Network

Published:2024 Issue: Volume:73 Page:1-14
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Chu Zhenzhong¹^ORCID,Gu Zhenhao²^ORCID,Chen Yunsai³^ORCID,Zhu Daqi¹^ORCID,Tang Jialing⁴^ORCID

Affiliation:

1. School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, China

2. Logistics Engineering College, Shanghai Maritime University, Shanghai, China

3. College of Shipbuilding Engineering, Harbin Engineering University, Harbin, China

4. Technology Department, National Deep Sea Center, Qingdao, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/19/10367905/10568192.pdf?arnumber=10568192

Reference36 articles.

1. A Review of Risk Analysis Research for the Operations of Autonomous Underwater Vehicles

2. Review on fault diagnosis of unmanned underwater vehicles

3. Research progress on thruster fault diagnosis technology for deep-sea underwater vehicle;Chen;J. Propuls. Technol.,2020

4. Feature extraction and severity identification for autonomous underwater vehicle with weak thruster fault

5. Thruster fault diagnosis method based on Gaussian particle filter for autonomous underwater vehicles

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Review of imbalanced fault diagnosis technology based on generative adversarial networks;Journal of Computational Design and Engineering;2024-08-31

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